Intelligent management of bike sharing in smart cities using machine learning and Internet of Things

Alaoui, El Arbi Abdellaoui; Tékouabou, Stéphane Cédric Koumétio

doi:10.1016/j.scs.2020.102702

Cited by 39 publications

(21 citation statements)

References 75 publications

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“…Traffic jams caused urban Americans to travel extra 8.8 billion hours and purchase extra 3.3 billion gallons of fuel for a congestion cost of $166 billion [4]. Compared to motorized transport, BSS provides an alternative to short-distance travel, effectively addresses the last mile travel problem, and significantly reduces traffic accidents and congestion [5].…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Cao

2022

Wireless Communications and Mobile Computing

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With the rapid development of information technology, the sharing economy has developed rapidly all over the world as a new mode of distributing business profit, among which the bike-sharing system (BSS) has become popular in many cities because of its low cost, convenience, and environmental protection. The application of the 5th generation mobile communication technology (5G) in BSS makes users to search the bikes more accurately and quickly and enables operators to spot noncompliant bike sharing as soon as possible, significantly improving the efficiency of bike-sharing management. However, one of the thorny issues for operators is the bike-sharing rebalancing problem (BRP). It is the key to improve the efficiency of rebalancing, reduce the rebalancing cost, and realize the sustainable development of BSS on how to excavate the huge amount of customer cycling data, respond quickly to customer demand, and use intelligence optimization algorithm to rebalance bikes among stations. However, most of the previous studies dealt with only one period BRP and rarely considered multiperiod issues. At the same time, most researches have focused on minimizing the total cost or time of rebalancing or customer dissatisfaction, but few have aimed at minimizing the rebalancing amount. In addition, the demand gap can reflect the real rental and returning requirements of customers over a certain period of time, which is rarely considered in solving BRP. First of all, this paper presents a multiperiod and multiobjective bike-sharing rebalancing problem (MMBRP). Secondly, a mathematical model is formulated with the objective of minimizing both the total rebalancing cost and amount. In order to solve MMBRP, an improved multiobjective backtracking search genetic algorithm (IMBSGA) is designed. Finally, the effectiveness and competitiveness of IMBSGA in solving MMBRP are verified by numerous experiments comparing with state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Cao

2022

Wireless Communications and Mobile Computing

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“…In [28], the researchers set the problem of predicting the number of bikes shared per hour, day and month in London with machine learning regressors techniques. The algorithms used comprehend RF, Bagging regressor (BGR), XGBoost, and Ada Boosting (AB) regressor.…”

Section: Related Workmentioning

confidence: 99%

“…What is similar is the consideration on the models indeed the deep learning techniques achieved better results in both works than ensemble learning techniques. The works regarding the station's demand [28], [27] considered ensemble learning techniques, that in our case studied have been outperformed by the deep learning strategies. Also [26] uses ensemble learning techniques and as prediction target a cumulated metric, the number of bikes rented in the bike-sharing system, while we predict a punctual value that is relevant for the consumer and it is a more complex data to be predicted being a disaggregated information per rack.…”

Section: B Comparison With the State Of The Artmentioning

confidence: 99%

Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations

Collini

Nesi²,

Pantaleo

2021

IEEE Access

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Bike-sharing is adopted as a valid alternative to traditional public transports since they are ecofriendly, prevent traffic congestions, reduce the probability of social contacts which happens on most of the public means. However, some problems may occur such as the irregular distribution of bikes on the stations/racks/areas, and the difficulty of knowing in advance the rack status with a certain degree of confidence, whether there will be available bikes at a specific bike-station at a certain time of the day, or a free slot for leaving the rented bike. Thus, providing predictions can be useful to improve the quality of service, especially in those cases in which the bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bikeslots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions of 15, 30, 45, and 60 minutes. The study demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offer a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. The paper also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different behaviours of the clusters. The solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure.

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“…After the first Bike-Sharing System (BSS) appeared in Amsterdam, the Netherlands, the system quickly spread around the world because of its flexibility, economy, and convenience [3]. Compared to motorized transport, BSS provides an alternative to short distance travel, effectively addresses the last mile travel problem, significantly reduces traffic accidents and congestion [4].…”

Section: Introductionmentioning

confidence: 99%

Bike-Sharing Fleet Allocation Optimization Based on Demand Gap and Cycle Rebalancing Strategies

Cao

Wang

2022

Scientific Programming

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In Bike-Sharing System (BSS), the initial number of bikes at station will affect the time interval and the amount of rebalancing, which is usually empirically determined and does not reflect the characteristics of consumer demand in finer time granularity, thus possibly leading to biased conclusions. In this paper, a fleet allocation method considering demand gap is first proposed to calculate the initial number of bikes at each station. Then, taking the number of demand gap periods as the decision variable, an optimization model is built to minimize the total rebalancing amount. Furthermore, the research periods are divided into multiple subcycles, the single-cycle and multicycle rebalancing strategies are presented, and the additional subcycle rebalancing method is introduced to amend the number of bikes between subcycles to decrease the rebalancing amount of the next subcycle. Finally, our methods are verified in effectively decreasing the rebalancing amount in a long-term rebalancing problem.

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Intelligent management of bike sharing in smart cities using machine learning and Internet of Things

Cited by 39 publications

References 75 publications

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

A New Intelligent Rebalancing Management Method for Multiperiod and Multiobjective Bike-Sharing System Based on Machine Learning-Enabled Signal Processing Techniques

Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations

Bike-Sharing Fleet Allocation Optimization Based on Demand Gap and Cycle Rebalancing Strategies

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